sdnet: Soft-Discretization-Based Bayesian Network Inference

Fitting discrete Bayesian networks using soft-discretized data. Soft-discretization is based on mixture of normal distributions. Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence.

Version: 2.3.8
Depends: R (≥ 3.0.2)
Imports: methods, stats, utils, graphics
Published: 2016-05-09
Author: Nikolay Balov
Maintainer: Nikolay Balov <nhbalov at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: sdnet results


Reference manual: sdnet.pdf
Package source: sdnet_2.3.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: sdnet_2.3.8.tgz
OS X Mavericks binaries: r-oldrel: sdnet_2.3.8.tgz
Old sources: sdnet archive


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